Description

Firecracker is an open-source virtualization technology that is purpose-built for creating and managing secure, multi-tenant container and function-based services.

Integrating Firecracker with a CF container runtime will bring security to the underlying Kubernetes cluster and to other apps when using the Cloud Application Platform.


Links (don't share them!)

Planning board

Looking for hackers with the skills:

kvm networking virtualization kubernetes

This project is part of:

Hack Week 19

Activity

  • almost 6 years ago: EDiGiacinto liked this project.
  • almost 6 years ago: tassis added keyword "kvm" to this project.
  • almost 6 years ago: tassis added keyword "networking" to this project.
  • almost 6 years ago: tassis added keyword "virtualization" to this project.
  • almost 6 years ago: tassis added keyword "kubernetes" to this project.
  • almost 6 years ago: dfaggioli liked this project.
  • almost 6 years ago: tassis started this project.
  • almost 6 years ago: tassis originated this project.

  • Comments

    Be the first to comment!

    Similar Projects

    A CLI for Harvester by mohamed.belgaied

    Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI. Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.

    Project Description

    Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as: harvester vm create my-vm --count 5 to create 5 VMs named my-vm-01 to my-vm-05.

    asciicast

    Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.

    Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli

    Done in previous Hackweeks

    • Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
    • Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE

    Goal for this Hackweek

    The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.

    Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it

    Issue list is here: https://github.com/belgaied2/harvester-cli/issues

    Resources

    The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact). Welcome contributions are:

    • Testing it and creating issues
    • Documentation
    • Go code improvement

    What you might learn

    Harvester CLI might be interesting to you if you want to learn more about:

    • GitHub Actions
    • Harvester as a SUSE Product
    • Go programming language
    • Kubernetes API
    • Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)


    SUSE KVM Best Practices - Focus on SAP Workloads and Use Cases by roseswe

    Description

    SUSE Best Practices around KVM, especially for SAP workloads. Early Google presentation already made from various customer projects and SUSE sources.

    Goals

    • Complete presentation we can reuse in SUSE Consulting projects
    • 2025: Bring it to version 1.00 ready for customers

    Resources

    KVM (virt-manager) images

    SUSE/SAP/KVM Best Practices

    • https://documentation.suse.com/en-us/sles/15-SP6/single-html/SLES-virtualization/
    • SAP Note 1522993 - "Linux: SAP on SUSE KVM - Kernel-based Virtual Machine" && 2284516 - SAP HANA virtualized on SUSE Linux Enterprise hypervisors https://me.sap.com/notes/2284516
    • SUSECon24: [TUTORIAL-1253] Virtualizing SAP workloads with SUSE KVM || https://youtu.be/PTkpRVpX2PM
    • SUSE Best Practices for SAP HANA on KVM - https://documentation.suse.com/sbp/sap-15/html/SBP-SLES4SAP-HANAonKVM-SLES15SP4/index.html


    SUSE KVM Best Practices - Focus on SAP Workloads and Use Cases by roseswe

    Description

    SUSE Best Practices around KVM, especially for SAP workloads. Early Google presentation already made from various customer projects and SUSE sources.

    Goals

    • Complete presentation we can reuse in SUSE Consulting projects
    • 2025: Bring it to version 1.00 ready for customers

    Resources

    KVM (virt-manager) images

    SUSE/SAP/KVM Best Practices

    • https://documentation.suse.com/en-us/sles/15-SP6/single-html/SLES-virtualization/
    • SAP Note 1522993 - "Linux: SAP on SUSE KVM - Kernel-based Virtual Machine" && 2284516 - SAP HANA virtualized on SUSE Linux Enterprise hypervisors https://me.sap.com/notes/2284516
    • SUSECon24: [TUTORIAL-1253] Virtualizing SAP workloads with SUSE KVM || https://youtu.be/PTkpRVpX2PM
    • SUSE Best Practices for SAP HANA on KVM - https://documentation.suse.com/sbp/sap-15/html/SBP-SLES4SAP-HANAonKVM-SLES15SP4/index.html


    Extracting, converting and importing VMs from Nutanix into SUSE Virtualization by emendonca

    Description

    The idea is to delve into understanding Nutanix AHV internals on how it stores and runs VMs, and how to extract them in an automated way for importing into a KVM-compatible hypervisor, like SUSE Virtualization/Harvester. The final product will be not only be documentation, but a working prototype that can be used to automate the process.

    Goals

    1) document how to create a simple lab with NutaniX AHV community edition 2) determine the basic elements we need to interact with 3) determine what are the best paths to grab the images through, balancing speed and complexity 4) document possible issues and create a roadmap for tackling them 4) should we adapt an existing solution or implement a new one? 5) implement the solution!

    Resources

    Similar project I created: https://github.com/doccaz/vm-import-ui Nutanix AHV forums Nutanix technical bulletins


    Q2Boot - A handy QEMU VM launcher by amanzini

    Description

    Q2Boot (Qemu Quick Boot) is a command-line tool that wraps QEMU to provide a streamlined experience for launching virtual machines. It automatically configures common settings like KVM acceleration, virtio drivers, and networking while allowing customization through both configuration files and command-line options.

    The project originally was a personal utility in D, now recently rewritten in idiomatic Go. It lives at repository https://github.com/ilmanzo/q2boot

    Goals

    Improve the project, testing with different scenarios , address issues and propose new features. It will benefit of some basic integration testing by providing small sample disk images.

    Resources


    Cluster API Provider for Harvester by rcase

    Project Description

    The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.

    The project has been bootstrapped in HackWeek 23, and its code is available here.

    Work done in HackWeek 2023

    • Have a early working version of the provider available on Rancher Sandbox : *DONE *
    • Demonstrated the created cluster can be imported using Rancher Turtles: DONE
    • Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo

    DONE in HackWeek 24:

    DONE in 2025 (out of Hackweek)

    • Support of ClusterClass
    • Add to clusterctl community providers, you can add it directly with clusterctl
    • Testing on newer versions of Harvester v1.4.X and v1.5.X
    • Support for clusterctl generate cluster ...
    • Improve Status Conditions to reflect current state of Infrastructure
    • Improve CI (some bugs for release creation)

    Goals for HackWeek 2025

    • FIRST and FOREMOST, any topic is important to you
    • Add e2e testing
    • Certify the provider for Rancher Turtles
    • Add Machine pool labeling
    • Add PCI-e passthrough capabilities.
    • Other improvement suggestions are welcome!

    Thanks to @isim and Dominic Giebert for their contributions!

    Resources

    Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.

    This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:


    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo


    Description

    The Problem

    Running LLMs can get expensive and complex pretty quickly.

    Today there are typically two choices:

    1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
    2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

    What if there was a middle ground?

    Project Repository: github.com/alexander-demicev/llmserverless


    What This Project Does

    A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

    A complete, self-scaling LLM infrastructure that:

    • Scales to zero when idle (no idle costs)
    • Scales up automatically when requests come in
    • Adds more nodes when needed, removes them when demand drops
    • Runs on any infrastructure - laptop, bare metal, or cloud

    Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

    How It Works

    A combination of open source tools working together:

    Flow:

    • Users interact with OpenWebUI (chat interface)
    • Requests go to LiteLLM Gateway
    • LiteLLM routes requests to:
      • Ollama (Knative) for local model inference (auto-scales pods)
      • Or cloud APIs for fallback
    • Cluster Autoscaler scales nodes up/down as needed
    • Fleet keeps everything in sync via GitOps

    Goals


    Rancher/k8s Trouble-Maker by tonyhansen

    Project Description

    When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.

    Goals for Hackweek 25

    • Update to modern Rancher and verify that existing tests still work
    • Change testing logic to populate secrets instead of requiring a secondary script
    • Add new tests

    Goals for Hackweek 24 (Complete)

    • Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix
    • Create at least 5 modules that can be applied to the cluster and require troubleshooting

    Resources

    • https://github.com/celidon/rancher-troublemaker
    • https://github.com/rancher/terraform-provider-rancher2
    • https://github.com/rancher/tf-rancher-up
    • https://github.com/rancher/quickstart


    The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio

    Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. A GitHub robot mascot trying to lasso a blue bull with a Kubernetes logo tatooed on it


    The Plan

    Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!

    Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:


    The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.

    The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.

    Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.


    If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.

    Why?

    We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

    The Outputs

    ❥ A "State of the Agentic Union" for SUSE engineers, detailing what works, what explodes, and how much coffee we can drink while the robots do the rebasing.

    ❥ Honest, Daily Updates With All the Gory Details


    Technical talks at universities by agamez

    Description

    This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

    For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

    Goals

    • Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
    • Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
    • Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

    Resources

    • Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
    • SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.